Geodesic
High-dimensional statistics, with applications to genome-wide association studies
Peter Bühlmann1
1ETH Zürich,Switzerland
EMS surveys in mathematical sciences, Tome 4 (2017) no. 1, pp. 45-75

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DOI

We present a selective review on high-dimensional statistics where the dimensionality of the unknown parameter in a model can be much larger than the sample size in a dataset (e.g. the number of people in a study). Particular attention is given to recent developments for quantifying uncertainty in high-dimensional scenarios. Assessing statistical uncertainties enables to describe some degree of replicability of scientific findings, an ingredient of key importance for many applications. We also show here how modern high-dimensional statistics offers new perspectives in an important area in genetics: novel ways of analyzing genome-wide association studies, towards inferring more causal-oriented conclusions.
DOI : 10.4171/emss/4-1-3
Classification : 62-XX, 60-XX
Mots-clés : De-sparsified Lasso, hierarchical multiple testing, Lasso, l1​-norm regularization, sparsity

Peter Bühlmann  1

1 ETH Zürich,Switzerland 
Peter Bühlmann. High-dimensional statistics, with applications to genome-wide association studies. EMS surveys in mathematical sciences, Tome 4 (2017) no. 1, pp. 45-75. doi: 10.4171/emss/4-1-3
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